@inproceedings{santos-rodriguez-graff-2025-infotec,
    title = "{INFOTEC}-{NLP} at {S}em{E}val-2025 Task 11: A Case Study on Transformer-Based Models and Bag of Words",
    author = "Santos - Rodriguez, Emmanuel  and
      Graff, Mario",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.50/",
    pages = "350--356",
    ISBN = "979-8-89176-273-2",
    abstract = "Leveraging transformer-based models as feature extractors, we introduce a hybrid architecture that integrates a bidirectional LSTM network with a multi-head attention mechanism to address the challenges of multilingual emotion detection in text. While pre-trained transformers provide robust contextual embeddings, they often struggle with capturing long-range dependencies and handling class imbalances, particularly in low-resource languages. To mitigate these issues, our approach combines sequential modeling and attention mechanisms, allowing the model to refine representations by emphasizing key emotional cues in text."
}